1. Field of the Invention
The present invention relates to a method for transferring process control models between plasma processing chambers.
2. Prior Art
The manufacture of integrated circuits is a detailed process requiring many complex steps. A typical semiconductor manufacturing plant (or fab) can require several hundred highly complex plasma processing chambers to fabricate intricate devices such as microprocessors or memory chips. These fabs typically construct these devices on a substrate of silicon, known as a silicon wafer. A single wafer, containing many such similar integrated circuits, often requires over 200 individual steps to complete the manufacturing process. These steps include lithographic patterning of the silicon wafer to define each device, etching lines to create structures and filling gaps with metal or dielectric to create the electrical device of interest. From start to finish the process can take weeks to complete.
On each chamber the wafer is processed according to some recipe, which is controlled by the tool operator. This recipe includes input parameter settings such as process gas flow rates, chamber pressure, substrate/wall temperatures, RF power settings on one or more power generators, recipe time, inter-electrode spacing, etc. This is the case for all plasma processing tools, such as etch, deposition, etc. The wafer will undergo very many plasma process steps before completion. Each step contributes to the overall product yield; a fault at any one step may destroy potential product.
FIG. 1 shows a typical plasma process reactor. It includes a plasma chamber 1 containing a wafer or substrate 2 to be processed. A plasma is established and maintained within the chamber by an RF power source 3. This source generally has real impedance which must undergo a transformation to match that of the complex plasma load. This is done via match network 4. Power is coupled to the plasma chamber, typically by capacitive or inductive coupling, through an electrode 8. Process gases are admitted through gas inlet 7 and the chamber is maintained at a desirable pressure by a pump 11 pumping through gas exhaust line 10. A throttle valve 9 may be used to control pressure. The plasma permits effective manufacture of for example, semiconductor devices, by changing gas chemistry. Gases such as Cl2, used to etch silicon and metal, for example, are converted into reactive and ionized species. Etching of the very fine geometry used to fabricate semiconductor devices is made possible by the reactive gases, ions and electrons of the plasma.
Referring again to FIG. 1, an RF sensor 5 is used to measure the RF electrical power signal in the complex post-match electrical line. A Fourier Transform is performed in data collection electronics 6 using a sampling technique which extracts the Fourier components of the voltage and current and the phase angle between these vectors. This data sampling should have sufficiently high resolution to determine the Fourier components across a very large dynamic range. Suitable techniques for high resolution sampling and measurement of the Fourier components are described in U.S. Pat. No. 5,808,415.
The output of the data collection electronics 6 is connected to a controller 12 which may be a computer or other system which uses the signals to yield information about and/or control the plasma process.
The Fourier components are very sensitive to plasma events. The wafer fabrication process involves running entire batches of wafers with similar plasma process recipes to ensure reliable volume production. If the plasma process on each wafer is the same, then the measured Fourier components will reflect this. Any change in the plasma process will be registered by change(s) in the Fourier components.
Key goals in a manufacturing plant are to maximise line yield (the percentage of wafers successfully processed) and die yield (the number of fully functioning devices on each wafer). Various process control mechanisms are used to optimise the performance of each tool to meet these objectives. One approach to process control is to apply a fault detection and classification (FDC) scheme to a fingerprint obtained from a sensor, such as the RF sensor described above.
An FDC scheme can be implemented as follows. First, the state of the process is measured using one or more sensors.
The sensor data can be multidimensional data from a single sensor (e.g., Fourier components of the RF source 3 obtained from the sensor 5, as in the embodiment to be described), or data from a set of sensors, but in either case the data must be sensitive to hardware and process changes. The important criterion is that the sensor data has sufficient dimensions to permit a plurality of different fingerprints to be defined for a respective plurality of different fault conditions. The RF sensor described above fulfils these requirements. As used herein, a “fingerprint” is a set of sensor data which defines a particular state of the equipment—thus a fault fingerprint means a set of sensor data defining the state of the equipment in a fault condition. As wafers are processed through the chamber, the fingerprint is analysed to determine if the process is in an abnormal state. If such a state is detected, wafer processing is halted until the problem is resolved. This is the fault detection step. The time taken to solve the problem and restore the tool to production can be reduced by further classifying the abnormal process state against a historical record of known fault conditions to determine if a similar fault has re-occurred. This is the fault classification step.
A method for learning the profile of sensor data associated with a specific type of fault is described in U.S. Pat. No. 6,441,620 and is explained using the following simple example.
A designed experiment (DOE) is carried by varying three process inputs: power, pressure and electrode spacing. The DOE design is shown in FIG. 2. In addition, the experiment includes a fault condition whereby a process kit part of the wrong dimension is installed in the chamber. The process kit is a part of the chamber hardware that is replaced during preventive maintenance. Installing an incorrect part alters wafer processing conditions which can cause wafers to be scrapped. An RF impedance sensor is used to measure the subsequent changes in the plasma chamber.
FIG. 3 shows how three of the sensor outputs, A1 (RF voltage fundamental), A2 (first harmonic of RF voltage) and A3 (RF phase fundamental), respond to changes in power and electrode spacing on a given chamber. Sensor outputs Al and A2 respond in a similar fashion to changes in power and spacing, but sensor output A3 responds differently. Thus, especially when all sensor outputs are taken into account, a change in process power will be different and distinguishable from a change in electrode spacing. If many of the hardware conditions and process inputs are changed in a design of experiments then a comprehensive tool profile comprising a set of sensor responses for all hardware and process input changes can be established, with each fault condition having its own unique fingerprint. A collection of sets of sensor data defining respective different fault conditions is referred to hereafter as a “fault library”. Each fault-defining set of sensor data in the fault library is preferably recorded as a set of differences from the relevant sensor outputs when the process is in a known good state.
To perform fault detection and classification, changes in sensor outputs are recorded for each wafer as it is processed. The difference between the current sensor outputs, and the sensor outputs when the process was in the known good state, is compared to the fault library. A match is considered to have been found if the fingerprint of the wafer is well correlated to a fault fingerprint in the fault library. Typically, the user is presented with a chart which shows how the current wafer correlates to all fingerprints in the library. An example of such a chart is shown in FIG. 4. This indicates that the current wafer is well correlated to the process kit fault fingerprint in the library, and the user can have good confidence that the FDC system has found an accurate match.
The magnitude of the change can be used to perform fault detection. If the change exceeds a user-specified threshold, a fault detection decision is reported to the user. FIG. 5 shows the magnitude of change for a sequence of wafers processed with the wrong process kit part installed. These wafers are above a fault detection threshold which has been set so as not to cause any false alarms on known good wafers.
A typical wafer fab has many plasma chambers dedicated to each step of the process and each chamber requires a fault library. In some cases, it may not be possible to copy the fault library learned on one chamber directly to all other nominally identical chambers, and use it for fault detection and classification. FIG. 6 shows what happens when an identical process kit fault to that induced in the chamber referred to in relation to FIGS. 3 to 5, Chamber A, is induced on a second chamber, Chamber B, nominally identical to Chamber A, where the fault library has been copied directly from Chamber A. The fault detection signal has been reduced by a factor of 5. The reason for the reduced sensitivity can be seen in FIG. 7 which shows the corresponding correlation information. Identical faults induced on Chamber A and Chamber B are not well correlated.
Thus, this approach to process control has two limitations.
Decreased sensitivity: The robustness of the FDC system depends on a large signal-to-noise ratio for each fault compared to the baseline of normal behaviour. Reducing the signal-to-noise ratio of the FDC system can lead to faults being missed, with a subsequent impact to line yield, die yield and cost.
Reduced accuracy of classification: The trustworthiness of fault classification information depends on achieving a good match to a previously learned pattern when a similar fault re-occurs. If the same fault re-occurs but does not trigger a reasonably close match to a pattern in the fault library, a user's confidence in the FDC system is diminished.
An alternative strategy would be to build a new copy of the fault library on each chamber. This method also has a major limitation. As each fault is required to be learned through a designed experiment carried out while the chamber is not running production material, this method would require significant downtime and is very costly. This is especially true for those experiments which require chamber hardware to be temporarily modified, as is the case with the process kit fault described above.
It is also possible to construct a fault library using only those sensor parameters which are known to respond similarly across different chambers. This approach would also lead to reduced sensitivity as other parameters that contain useful information about a fault condition are not used.
Therefore, there is a need for a method for manipulating a fault library in some fashion so that it can be used on multiple chambers without sacrificing sensitivity.